Build an AI Chatbot with Python Using RAG, LangChain, Ollama

About this course

Course Description:

Welcome to "Building a RAG Application with Ollama, LangChain, and Vector Embeddings in Python"! This hands-on course is designed for Python developers, data scientists, and AI enthusiasts looking to dive into the world of Retrieval-Augmented Generation (RAG) and learn how to build intelligent document-based applications.

In this course, you will learn how to create a powerful PDF Q&A chatbot using state-of-the-art AI tools like Ollama, LangChain, and Vector Embeddings. You'll gain practical experience in processing PDF documents, extracting and generating meaningful information, and integrating a local Large Language Model (LLM) to provide context-aware responses to user queries.

What you will learn:

• What is RAG (Retrieval-Augmented Generation) and how it enhances the power of LLMs
• How to process PDF documents using LangChainExtracting text from PDFs and splitting it into chunks for efficient retrieval
• Generating vector embeddings using semantic search for better accuracy
• How to query and retrieve relevant information from documents using Vector DBIntegrating a local LLM with Ollama to generate context-aware responses
• Practical tips for fine-tuning and improving AI model responses

Course Highlights:

• Step-by-step guidance on setting up your development environment with VS Code, Python, and necessary libraries.
• Practical projects where you’ll build a fully functional PDF Q&A chatbot from scratch.
• Hands-on experience with Ollama (a powerful tool for running local LLMs) a

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.